本文選題：大豆油 + 反式脂肪酸�。� 參考：《哈爾濱商業(yè)大學(xué)》2017年碩士論文

【摘要】：食用油是人類膳食的重要組成部分,其質(zhì)量安全對人體健康至關(guān)重要。近年來,食用油脂中反式脂肪酸含量超標問題尤為嚴重,已引起社會各界的廣泛關(guān)注。目前,我國針對反式脂肪酸含量的測定方法普遍存在消耗化學(xué)試劑多、檢測速度慢、測定程序復(fù)雜、需要對樣品進行復(fù)雜的前期處理等問題,難以滿足現(xiàn)代社會對食用油質(zhì)量快速、準確、簡便、現(xiàn)場化的測定要求。太赫茲光譜分析技術(shù)可以解決傳統(tǒng)方法的諸多問題,更適用于油脂生產(chǎn)過程中的品質(zhì)監(jiān)控,因此本文提出基于太赫茲光譜技術(shù)檢測食用油中反式脂肪酸含量的方法,以我國第一大食用油——大豆油為例,重點對反式脂肪酸含量太赫茲光譜分析中的數(shù)據(jù)處理以及建模方法進行深入研究。首先,制備出不同反式脂肪酸含量的大豆油脂樣品34個,利用氣相色譜儀精確測定了其反式脂肪酸含量,同步采集了樣品的太赫茲時域譜并利用傅里葉變換將其轉(zhuǎn)換為頻域譜,然后通過光學(xué)參數(shù)計算得出太赫茲吸收譜以及折射譜。在分析了大豆油脂太赫茲光譜吸收特點的基礎(chǔ)上,剔除異常樣品1個。將33個樣品進行了分集,按反式脂肪酸含量的多少進行排序,從中選取28個作為訓(xùn)練集樣品建立校正模型,剩下的5個作為預(yù)測集樣品對模型進行驗證;為了尋找最佳模型,分別利用偏最小二乘(PLS)、支持向量機回歸(SVR)和最小二乘支持向量機(LS-SVR)三種方法建模并對比分析,最終確定建模效果最好的是LS-SVR,預(yù)測誤差均方根RMSEP為0.3246,決定系數(shù)R2為0.9792,相對標準差RSD為3.81%,可以滿足實際檢測要求;為了進一步提高模型的預(yù)測精度,分別采用網(wǎng)格搜索法、粒子群算法(PSO)、遺傳算法(GA)對LS-SVR模型參數(shù)進行優(yōu)化,并對優(yōu)化結(jié)果進行對比分析,發(fā)現(xiàn)粒子群(PSO)算法對LS-SVR模型參數(shù)優(yōu)化的效果更佳、更穩(wěn)定,預(yù)測誤差均方根(RMSEP)、決定系數(shù)(R2)和預(yù)測相對標準偏差(RSD)分別達到0.0763、0.9989和0.90%,模型的預(yù)測精度得到了顯著提高。本文研究證明了利用太赫茲光譜檢測油脂中反式脂肪酸含量的可行性,為開發(fā)專用油脂太赫茲光譜分析儀器及實現(xiàn)在線檢測奠定了理論基礎(chǔ)。
[Abstract]:Edible oil is an important part of human diet, its quality and safety is very important to human health. In recent years, the problem of trans fatty acids in edible oils is especially serious, which has attracted wide attention from all walks of life. At present, there are many problems in the determination of trans fatty acids in China, such as the consumption of chemical reagents, the slow detection speed, the complexity of the determination procedures, and the need for complex pre-treatment of the samples. It is difficult to meet the requirements of fast, accurate, simple and field determination of edible oil in modern society. Terahertz spectroscopy can solve many problems of traditional methods and is more suitable for quality control in oil production. Therefore, a method based on terahertz spectroscopy to detect trans fatty acid content in edible oil is proposed in this paper. Taking soybean oil, the largest edible oil in China, as an example, the data processing and modeling method in THz spectrum analysis of trans fatty acid content were studied in detail. First of all, 34 soybean oil samples with different trans fatty acid contents were prepared, and their trans fatty acids were accurately determined by gas chromatograph. The terahertz time-domain spectra of the samples were simultaneously collected and converted into frequency domain spectra by Fourier transform. Then the terahertz absorption spectrum and refraction spectrum are calculated by optical parameters. On the basis of analyzing the absorption characteristics of terahertz spectrum of soybean oil, one abnormal sample was eliminated. In order to find the best model, 33 samples were sorted according to the content of trans fatty acids, 28 samples were selected as training set samples to establish calibration model, and the remaining 5 samples were used as predictive set samples to verify the model. Three methods, partial least squares (PLS), support vector machine regression (SVR) and least squares support vector machine (LS-SVR), are used to model and analyze the model. Finally, LS-SVR is the best model, the root mean square (RMSEP) of prediction error is 0.3246, the coefficient of determination (R2) is 0.9792, and the relative standard deviation (RSD) is 3.81. in order to further improve the prediction accuracy of the model, the grid search method is used. Particle Swarm Optimization (PSO), genetic algorithm (GA), is used to optimize the parameters of LS-SVR model, and the results are compared and analyzed. It is found that PSO (Particle Swarm Swarm Optimization) algorithm is more effective and stable in the optimization of LS-SVR model parameters. The mean square error (RMS), the determination coefficient (R2) and the relative standard deviation (RSD) of prediction are 0.0763 ~ 0.9989 and 0.90, respectively. The prediction accuracy of the model has been improved significantly. In this paper, the feasibility of using terahertz spectrum to detect the content of trans fatty acids in oils has been proved, which lays a theoretical foundation for the development of a special THz spectrometer for oil analysis and the realization of on-line detection.
【學(xué)位授予單位】：哈爾濱商業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TS227;TP18

【參考文獻】

相關(guān)期刊論文 前10條

1 趙超;李俊;戴坤成;王貴評;;基于自適應(yīng)加權(quán)最小二乘支持向量機的青霉素發(fā)酵過程軟測量建模[J];南京理工大學(xué)學(xué)報;2017年01期

2 劉飛明;張雨飛;;采用改進混沌粒子群算法的鍋爐NO_x排放的LSSVM回歸建模[J];工業(yè)控制計算機;2017年01期

3 裴娟娟;周曉燕;榮汝繁;杜鵬程;陳志勇;;氣相色譜法測定食醋中環(huán)己基氨基磺酸鈉[J];食品研究與開發(fā);2017年02期

4 黃睿;;乘性噪聲干擾下太赫茲時域激光譜數(shù)據(jù)快速采集方法研究[J];激光雜志;2016年12期

5 王艷;沈曉宇;丁文勝;王健波;鄒秀陽;;基于PCNN和遺傳算法相結(jié)合的新型混凝土橋梁裂縫檢測方法[J];計算機應(yīng)用研究;2017年10期

6 王春榮;夏爾冬;吳龍;劉建軍;熊昌炯;;基于改進支持向量回歸算法的移動機器人定位[J];計算機應(yīng)用;2016年09期

7 肖理慶;王化祥;;利用改進粒子群算法優(yōu)化ERT有限元模型[J];計算機應(yīng)用研究;2017年05期

8 陳華舟;陳福;許麗莉;溫江北;李玲慧;;基于網(wǎng)格搜索的參數(shù)優(yōu)化方法用于魚粉灰分的近紅外LSSVM定量分析[J];分析科學(xué)學(xué)報;2016年02期

9 魏靜;;基于MATLAB和遺傳算法的車輛懸架參數(shù)模型優(yōu)化研究[J];機械制造;2016年03期

10 鄭妹鳳;張丹丹;;我國反式脂肪酸的研究現(xiàn)狀[J];安徽預(yù)防醫(yī)學(xué)雜志;2016年01期

相關(guān)博士學(xué)位論文 前2條

1 金文蔚;有機電光材料的太赫茲關(guān)鍵性能研究[D];電子科技大學(xué);2016年

2 李逸楠;基于太赫茲時域光譜技術(shù)的成品油定量識別方法研究[D];天津大學(xué);2014年

相關(guān)碩士學(xué)位論文 前8條

1 石巖;氣相色譜法測定茶鮮葉中有機氯和擬除蟲菊酯類農(nóng)藥殘留[D];山東農(nóng)業(yè)大學(xué);2016年

2 涂珊;太赫茲傅里葉變換光譜技術(shù)及其應(yīng)用研究[D];電子科技大學(xué);2016年

3 袁慧;太赫茲成像技術(shù)若干問題的研究[D];北京理工大學(xué);2016年

4 田碩;基于近紅外特征脂肪酸的摻假芝麻油的鑒別[D];河南科技大學(xué);2014年

5 趙容嬌;太赫茲譜技術(shù)在藥物分析中的應(yīng)用[D];中國計量學(xué)院;2013年

6 陽麗芝;乳脂中5種脂肪酸紅外快速檢測模型的研究[D];山東理工大學(xué);2011年

7 王銀年;遺傳算法的研究與應(yīng)用[D];江南大學(xué);2009年

8 楊昆;太赫茲時域光譜成像的研究[D];首都師范大學(xué);2009年

，

本文編號：1899751